1. Field
This disclosure relates to systems and methods for data mining.
2. Background
Data mining may be thought of as a process for extracting data, including implicit and potentially useful information, from a variety of data sources, such as a set of large but disparate databases. Generally, data mining involves reviewing large amounts of data, and subsequently picking that information which appears relevant and potentially valuable.
Initially, data mining was used primarily by business-related organizations and by financial analysts. However, the benefits of data mining have spurred its adoption in the various non-financial sciences as well.
Artificial Neural Networks (ANNs) have increasingly come to the aid of data mining applications. For example, a software application known as the Alyuda Forecaster by Ayluda Research Corporation® of Los Altos, Calif., has developed a trainable neural network that can work with Excel® Spreadsheets by Microsoft Corporation® of Redmond, Wash.
Despite the progress of ANN-enhanced data mining systems, known ANNs suffer from a number of deficiencies. For example, known ANNs cannot perform the logical “modus ponens” operation, and thus it follows that ANNs may be unsuited for certain data mining operations. Accordingly, new data mining technologies are desirable.